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Resources

Resources

A repository of data, publications, tools, and other products from project teams, Science Collaborative program, and partners.

Displaying 41 - 50 of 104
Multimedia |

This story map and K-12 activity invites students to explore coastal marsh vulnerability to sea level rise and a collaborative experiment to enhance marsh resilience at the Chesapeake Bay National Estuarine Research Reserve in Virginia.

Factsheet |

The Native Olympia Oyster Collaborative brochure Restoring Resilient Native Oysters from Baja California to British Columbia provides an introduction to Olympia oyster restoration for general audiences.

News |

Webinar Summary |

This resource contains the presenter slides, Q&A responses, recording, and presenter bios from the July 2020 webinar Innovative Approaches to Integrating Research and K-12 Education to Advance Estuary Stewardship.

Project Overview |

This project overview describes a 2017 Science Transfer project in which the southeastern National Estuarine Research Reserves created a region-wide, student-driven program for teachers to further understanding of estuary restoration.

Project Overview |

This project overview describes a multi-year collaborative research project that analyzed a suite of living shoreline possibilities for South Carolina to help the state develop a living shoreline policy.

Project Overview |

This project overview describes a 2018 Catalyst project that created an Olympia oyster restoration network to enhance the success of West Coast restoration efforts.

Project Overview |

This project overview describes a 2017 science transfer project that developed a risk communication training for reserves to build risk communication capacity in four coastal communities.

Report |

This national synthesis report analyzes SET data from 15 National Estuarine Research Reserves across the continental United States, summarizing wetland water level trends over a 19-year period.

Tool |

A 2018 catalyst project developed tools for working with SET data including a series of computer codes - R scripts - for processing, quality checking, analyzing and visualizing these complex datasets. The statistical codes re available through GitHub and are explained in a Guide to the SETr Workflow.